Abstract: In various implementations, a device includes an environmental sensor, a non-transitory memory and one or more processors coupled with the environmental sensor and the non-transitory memory. In some implementations, a method includes, while the device is in a physical environment that includes a fixed spatial point and a person, obtaining, via the environmental sensor, spatial data corresponding to a-physical environment and the person. The method includes determining, based on the spatial data, a position of a first body portion of the person relative to the fixed spatial point. Determining the position of the first body portion is based on a distance between the device and the fixed spatial point and based on a distance between the device and the first body portion. The method includes generating, based on the position of the first body portion, pose information for the first body portion relative to the fixed spatial point.

Abstract: Various implementations disclosed herein include devices, systems, and methods for generating body pose information for a person in a physical environment. In various implementations, a device includes an environmental sensor, a non-transitory memory and one or more processors coupled with the environmental sensor and the non-transitory memory. In some implementations, a method includes obtaining, via the environmental sensor, spatial data corresponding to a physical environment. The physical environment includes a person and a fixed spatial point. The method includes identifying a portion of the spatial data that corresponds to a body portion of the person. In some implementations, the method includes determining a position of the body portion relative to the fixed spatial point based on the portion of the spatial data. In some implementations, the method includes generating pose information for the person based on the position of body portion in relation to the fixed spatial point.

Abstract: A computerized method for forecasting a future conformation of a molecular system based on a current conformation of the molecular system comprises (a) receiving the current conformation in a trained machine-learning model that has been previously trained to map a plurality of conformations received to a corresponding plurality of conformations proposed; (b) mapping the current conformation to a proposed conformation via the trained machine-learning model, wherein the proposed conformation is appended to a Markov chain; and (c) returning the proposed conformation as the future conformation.

Abstract: Examples are disclosed that relate to encoding data on a data-storage medium. The method comprises obtaining a representation of a measurement performed on the data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.

Abstract: The present invention relates to a process for preparing poly(3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) particles at least comprising the steps: a) providing a mixture comprising poly(3,4-ethylenedioxythiophene) and polystyrene sulfonate in a solvent at least comprising water; b) forming one or more PEDOT:PSS droplets by introducing the mixture from process step a) into an organic solvent A, wherein the aqueous PEDOT:PSS mixture forms the droplet interior and the organic solvent A forms the droplet exterior; c) contacting the PEDOT:PSS droplets obtained from process step b) with a coagulating solution comprising a curing agent and at least one further solvent B, the density of the coagulating solution being greater than the density of the organic solvent A and less than the density of the aqueous poly(3,4-ethylenedioxythiophene) and polystyrene sulfonate mixture; with curing of the PEDOT:PSS droplets to PEDOT:PSS particles.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

Abstract: A method in which the position of the center of gravity of a moving motor vehicle is ascertained, wherein at least one set of related input variables is taken into consideration, and the set of input variables includes at least a longitudinal acceleration of the motor vehicle, a lateral acceleration of the motor vehicle, a yaw rate of the motor vehicle and at least one wheel rotational speed, in particular four wheel rotational speeds, wherein the set of input variables is ascertained during a steady-state driving maneuver, and a quantity of possible center of gravity positions is defined as classes and, by a learning-based classification method, on the basis of the set of input variables, a class is selected which indicates an estimated center of gravity position. A control unit for carrying out the method is also disclosed.

Abstract: Various implementations disclosed herein include devices, systems, and methods for generating body pose information for a person in a physical environment. In various implementations, a device includes an environmental sensor, a non-transitory memory and one or more processors coupled with the environmental sensor and the non-transitory memory. In some implementations, a method includes obtaining, via the environmental sensor, spatial data corresponding to a physical environment. The physical environment includes a person and a fixed spatial point. The method includes identifying a portion of the spatial data that corresponds to a body portion of the person. In some implementations, the method includes determining a position of the body portion relative to the fixed spatial point based on the portion of the spatial data. In some implementations, the method includes generating pose information for the person based on the position of body portion in relation to the fixed spatial point.

Abstract: Various implementations disclosed herein include devices, systems, and methods for generating body pose information for a person in a physical environment. In various implementations, a device includes an environmental sensor, a non-transitory memory and one or more processors coupled with the environmental sensor and the non-transitory memory. In some implementations, a method includes obtaining, via the environmental sensor, spatial data corresponding to a physical environment. The physical environment includes a person and a fixed spatial point. The method includes identifying a portion of the spatial data that corresponds to a body portion of the person. In some implementations, the method includes determining a position of the body portion relative to the fixed spatial point based on the portion of the spatial data. In some implementations, the method includes generating pose information for the person based on the position of body portion in relation to the fixed spatial point.

Abstract: Examples are disclosed that relate to encoding data on a data-storage medium. The method comprises obtaining a representation of a measurement performed on the data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.

Abstract: Examples are disclosed that relate to reading stored data. The method comprises obtaining a representation of a measurement performed on a data-storage medium, the representation being based on a previously recorded pattern of data encoded in the data-storage medium in a layout that defines a plurality of data locations. The method further comprises inputting the representation into a data decoder comprising a trained machine-learning function, and obtaining from the data decoder, for each data location of the layout, a plurality of probability values, wherein each probability value is associated with a corresponding data value and represents the probability that the corresponding data value matches the actual data value in the previously recorded pattern of data at a same location in the layout.

Abstract: Various implementations disclosed herein include devices, systems, and methods for generating body pose information for a person in a physical environment. In various implementations, a device includes an environmental sensor, a non-transitory memory and one or more processors coupled with the environmental sensor and the non-transitory memory. In some implementations, a method includes obtaining, via the environmental sensor, spatial data corresponding to a physical environment. The physical environment includes a person and a fixed spatial point. The method includes identifying a portion of the spatial data that corresponds to a body portion of the person. In some implementations, the method includes determining a position of the body portion relative to the fixed spatial point based on the portion of the spatial data. In some implementations, the method includes generating pose information for the person based on the position of body portion in relation to the fixed spatial point.

Abstract: Memory facilitation using directed acyclic graphs is described, for example, where a plurality of directed acyclic graphs are trained for gesture recognition from human skeletal data, or to estimate human body joint positions from depth images for gesture detection. In various examples directed acyclic graphs are grown during training using a training objective which takes into account both connection patterns between nodes and split function parameter values. For example, a layer of child nodes is grown and connected to a parent layer of nodes using an initialization strategy. In examples, various local search processes are used to find good combinations of connection patterns and split function parameters.

Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.

Abstract: A method in which the position of the center of gravity of a moving motor vehicle is ascertained, wherein at least one set of related input variables is taken into consideration, and the set of input variables includes at least a longitudinal acceleration of the motor vehicle, a lateral acceleration of the motor vehicle, a yaw rate of the motor vehicle and at least one wheel rotational speed, in particular four wheel rotational speeds, wherein the set of input variables is ascertained during a steady-state driving maneuver, and a quantity of possible center of gravity positions is defined as classes and, by a learning-based classification method, on the basis of the set of input variables, a class is selected which indicates an estimated center of gravity position. A control unit for carrying out the method is also disclosed.

Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise one or more physical conditions of the user; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a circumstance when the user is exhibiting a particular physical condition to output subsequent questions.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

Abstract: A data-storage system comprises a head receiver configured to variably receive up to a number M of write heads. The data-storage system also includes an installed number N of write heads arranged in the head receiver, a substrate receiver configured to receive one or more data-storage substrates, and a positioner machine configured to adjust a relative placement of each of the M write heads with respect to at least one of the one or more data-storage substrates.